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Pavan Tikkisetty

@pavantikkisetty

Message

I build production-grade Lakehouse data platforms—metadata-driven ingestion, governance, and cost-optimized pipelines.

United Kingdom

Message

What I'm looking for

I’m looking for a role where I can architect metadata-driven Lakehouse pipelines with strong data quality, FCA-ready governance, and production reliability—using Databricks, ADF, and Delta Lake—while improving cost efficiency and developer onboarding speed.

Data Engineer with 6+ years delivering production-grade Lakehouse platforms across automotive and financial services. Currently at Mercedes-Benz UK, I architect metadata-driven ingestion frameworks across 50+ data sources, reducing onboarding time by 70% and cutting compute costs by 60% through Databricks job cluster optimisation.

I’m a specialist in Delta Lake, Unity Catalog, PySpark, ADF, and medallion architecture at scale (150+ tables), with hands-on experience in FCA regulatory reporting, SCD Type 2, and enterprise data quality frameworks. I standardise deployments via Databricks Asset Bundles and Azure DevOps CI/CD pipelines, while improving operational visibility with centralised ETL audit logging, reconciliation checks, and quarantine-based processing for invalid records.

Experience

Work history, roles, and key accomplishments

Current

Data Engineer

Current

Mercedes-Benz UK

May 2025 - Present (1 year 1 month)

Architected a metadata-driven Azure Databricks/Delta Lake lakehouse across 50+ data sources, cutting onboarding time from 2 weeks to under 3 days (70%+ reduction in development effort). Built quarantine-based enterprise data quality with Teams alerting (50% less debug-and-rerun effort), migrated to job clusters to reduce compute costs by 60%, and implemented SCD Type 2 + source-to-target reconcili

Azure Databricks Delta Lake Unity Catalog Azure Data Factory Pyspark Data Quality Governance Databricks Asset Bundles FCA Reporting

Data Engineer

Primary Residential Mortgage, Inc.

Sep 2022 - Apr 2025 (2 years 7 months)

Engineered parameterised Azure Data Factory pipelines for APIs, OData, SFTP, and enterprise databases with incremental loads, retry handling, schema drift controls, and financial data reconciliation. Optimized Azure SQL Data Warehouse workloads to reduce processing latency, implemented monitoring and alerting with Azure Monitor and Log Analytics, and built Power BI dashboards for business stakehol

Azure Data Factory Azure SQL REST APIs OData SFTP Python SQL Power BI

Data Engineer

Toyota Industries Commercial Finance

Aug 2020 - Aug 2022 (2 years)

Delivered production Azure data pipelines for real-time reporting and analytics, reducing data processing time by 50% through integration of Azure Data Factory and Azure Databricks. Implemented Azure SQL partitioning to reduce storage costs by 15%, achieved 100% compliance during ADLS security audits, and reduced downtime by 30% using Azure Monitor and Log Analytics.

Azure Data Factory Azure Databricks Azure SQL azure monitor Log Analytics Pyspark Tableau